Author

K. G. Sprenger

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Biophysics· 1 Immunology· 1

Signature Assays, Organisms & Methods

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Approach:statistical model· 2 Approach:deep learning· 1 Research Focus:drug response / drug discovery· 1 Organism:human· 1 Research Focus:protein folding & dynamics· 1 Assay:proteomics· 1

Frequent Co-Authors

A. K. Chakraborty· 1 shared B. J. Dekosky· 1 shared B. M. Petersen· 1 shared E. R. Rhodes· 1 shared J. E. Louveau· 1 shared M. F. Gutierrez Gonzalez· 1 shared S. A. Ulmer· 1 shared T. A. Whitehead· 1 shared

Activity by Year

2020-2021 · peak 1/yr

Co-Author Collaboration Network

8 top collaborators · 16 mutual co-author links

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1 shared paperBiophysics

A. K. Chakraborty

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2 papers

Recent·Most cited

ImmunologybioRxivJun 22, 2021

Regulatory approved monoclonal antibodies contain framework mutations predicted from human antibody repertoires

B. M. Petersen, S. A. Ulmer, E. R. Rhodes…·T. A. Whitehead

The paper predicts framework mutations in therapeutic monoclonal antibodies using natural human antibody repertoires, showing potential to improve antibody properties and reduce immunogenicity.

DOI 10.3389/fimmu.2021.728694

humanproteomics

𝕏 PostBluesky

BiophysicsbioRxivJan 4, 2020

Optimizing immunization protocols to elicit broadly neutralizing antibodies

K. G. Sprenger, J. E. Louveau, A. K. Chakraborty

This study uses computational models to show that sequential immunization protocols can optimize the evolution of broadly neutralizing antibodies against mutable pathogens.

DOI 10.1073/pnas.1919329117

deep learningstatistical model

𝕏 PostBluesky